When providing content to a user, systems often make recommendations based on per-user information. This information may include a user history and/or contextual information. Certain recommendation techniques such as collaborative filtering and other clustering techniques merely groups items of a history together when considering recommendations. For example, if items A, B, C, D, and E are often viewed as a group, and a user views any three of the five items, the other two will be given as recommendations. Such a system does not, however, consider an ordering when providing recommendations. Accordingly, these recommendations do not exploit the additional information that may be provided by accounting for a trajectory of the user history.